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Moringa oleifera is an herb that has the potential to reduce the mortality rate of an embryo. Research about Moringa oleifera treatment toward an embryo of livestock was quite a number. However, there were still very few previous studies that explain the trend of research related to that topic in this decade, 2010–2023. This study tried to observe the research trend related to Moringa oleifera treatment to embryos of livestock in terms of frequently used words inside papers along with their citations. This study gathered 132 data samples from Scopus and 41 data from PubMed and processed them using the bibliometric method. The bibliometric software used was Vosviewer to produce the image of the author's keyword connection and trend and biblioshiny for depicting frequently words used as a title and inside content and mean of total citation/year. This study also used R Studio to complement Vosviewer in conducting the bibliometric method. The result showed that there was no author's keyword that depicted ruminant-type animals instead of cow, and no name of the animal as livestock that was being used as a title of the sample papers. There were also no papers that observed Moringa oleifera to treat sick embryos of livestock and the previous studies used as samples also had a low mean of total citation/year.

Mulching with recycled paper has the potential to be used in agricultural production and can be adopted in strawberry cultivation. Therefore, the objective of this study was to evaluate the agronomic characteristics, water consumption and technical coefficients of strawberry cultivated with recycled paper as mulch. The treatments consisted of strawberry cultivation in lysimeters with recycled paper mulch and without soil cover. The recycled paper used was 187 μm-thick. The irrigation system was installed with drippers whose flow rate was 2 L h−1. Strawberry irrigation requirements were 317 and 394 mm, with and without mulch, respectively. Mulching with recycled paper did not have a significant effect on the average number of fruits, fresh fruit mass, fruit waste per plant, average fruit yield and water productivity. In relation to the technical coefficients, maximum values of the evaporation coefficient (Ke) of 0.40 (calculated with the evapotranspiration of the grass—L) and 0.28 (calculated with the Penman–Monteith ETo—PM) were obtained for OPM. The Kc values for the initial and full development stages were 0.31 and 0.84 (L) and 0.24 and 0.66 (PM), respectively. NDVI can be used to estimate strawberry Kc values. Recycled paper as mulch reduces the water consumption of strawberry crops and does not alter the agronomic characteristics. [ABSTRACT FROM AUTHOR]

While increasing numbers of studies report wide variations of leaf silicon (Si) accumulation among plant species, within-species variations of leaf Si accumulation have scarcely been examined for tree species. As in crop plants, environmental factors that affect transpiration rates may influence passive transpiration-dependent transport of Si uptake in trees. Here, we tested a hypothesis that leaf Si accumulation rate should be higher in shoots that receive more light and thus achieve faster growth, using Broussonetia papyrifera, a pioneer tree species with successive leaf production and Si accumulation with leaf age. We marked individual leaves weekly throughout the growing season (June–September), and measured Si concentration and light availability in relation to the chronosequence of leaf age in September. In shoots that continued growing and successively produced leaves throughout the growing season, leaf Si content increased linearly with leaf age. In support of our hypothesis, leaf Si accumulation rate varied widely among shoots with positive correlations with shoot growth and light availability. In conclusion, both leaf age and microenvironment affect within-species variations in leaf Si concentration of this species, a moderate Si accumulator. [ABSTRACT FROM AUTHOR]

Spider mites become easy prey for ants when they leave their protective webs; therefore, the ability to avoid traces of ongoing ant activity should confer a selective advantage to mites. We examined avoidance of ant traces by the spider mites Tetranychus kanzawai and Tetranychus urticae. Both mite species avoided host plant leaves with active traces of Pristomyrmex punctatus or Formica japonica ants. Pristomyrmex punctatus trace avoidance by T. kanzawai lasted for more than 1 h, but not more than 3 h. Tetranychus kanzawai also avoided P. punctatus traces on plant stems, along which the mites access leaves. Moreover, T. kanzawai avoided hexane extracts of P. punctatus or F. japonica applied to a filter paper pathway. This study represents the first demonstration of a repellent effect of ant chemical traces on spider mites. Considering the substantial abundance and global distribution of ants in nature, such repellent effects may help to answer the long-standing question of why only a small fraction of available plant resources is used by herbivores. Although spider mites have developed resistance against many synthetic pesticides, natural compounds that simulate ant chemical traces may repel spider mites from agricultural crops. [ABSTRACT FROM AUTHOR]

Pineapples are an important agricultural economic crop in Taiwan. Considerable human resources are required to protect pineapples from excessive solar radiation, which could otherwise lead to overheating and subsequent deterioration. Note that simple covering all of the fruit with a paper bag is not a viable solution, due to the fact that it makes it impossible to determine whether the fruit is ripe. This paper proposes a system by which to automate the detection of ripe pineapples. The proposed deep learning architecture enables detection regardless of lighting conditions, achieving accuracy of more than 99.27% with error of less than 2% at distances of 300 ~ 800 mm. This proposed system using an Nvidia TX2 is capable of 15 frames per second, thereby making it possible to mount the device on machines that move at walking speed. [ABSTRACT FROM AUTHOR]

The plant kingdom has various species that are underutilised. Proper scientific approached help them to utilised for multiple end applications. Okra is one of such underutilized multi-purpose vegetable crop. Green immature okra fruits are consumed as a vegetable worldwide. Okra seed is a good source of oil and protein. Okra seeds are also used as a substitute for coffee. Okra roots are used for clarification of sugarcane juice. It has medicinal value as well as useful in wastewater treatment. Okra is also a potential source of natural fibers suitable for textile, paper and other engineering applications. The present paper reviewed the okra plant, its cultivation and applications in various form such as food, medicine, textile fiber, paper pulp, biomass, etc. for its introduction as a multi-purpose vegetable crop. [ABSTRACT FROM AUTHOR]

In this work, we developed a systematic map to identify and catalogue the literature pertaining to disease modelling for agricultural crops worldwide. Searches were performed in 2021 in the Web of Science and Scopus for papers reporting any type of disease model for 103 crops. In total, 768 papers were retrieved, and their descriptive metadata were extracted. The number of papers found increased from the mid-1900s to 2020, and most of the studies were from North America and Europe. More disease models were retrieved for wheat, potatoes, grapes, and apples than for other crops; the number of papers was more affected by the crop's economic value than by its cultivated area. The systematic map revealed an underrepresentation of disease models for maize and rice, which is not justified by either the crop economic value or by disease impact. Most of the models were developed to understand the pathosystem, and fewer were developed for tactical disease management, strategic planning, or scenario analysis. The systematic map highlights a variety of knowledge gaps and suggests questions that warrant further research. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative IoT-enabled crop storage monitoring system designed to revolutionize the preservation and quality assurance of stored agricultural produce. Central to this system are IoT sensors strategically deployed within storage facilities. These sensors are tasked with the continuous tracking and monitoring of crucial environmental parameters, specifically temperature, humidity, and gas levels. Leveraging this real-time data, the system is engineered to promptly detect and respond to any deviations from the prescribed optimal storage conditions. The core strength of this system lies in its ability to generate instantaneous alerts upon detecting irregularities. These alerts serve as preemptive measures, effectively averting potential spoilage and curtailing post-harvest losses by enabling timely interventions and corrective actions. By harnessing IoT technology, this paper aims to create a proactive, automated, and responsive framework that ensures the integrity and safety of stored agricultural produce, ultimately contributing to enhanced food security and sustainability. [ABSTRACT FROM AUTHOR]

Agroforestry is often seen as a sustainable land-use system for agricultural production providing ecosystem services. Intercropping with food crops leads to equal or higher productivity than monoculture and results in food production for industry and subsistence. Low rubber price and low labor productivity in smallholdings have led to a dramatic conversion of rubber plantations to more profitable crops. The literature analysis performed in this paper aimed at better understanding the ins and outs that could make rubber-based agroforestry more attractive for farmers. A comprehensive search of references was conducted in March 2023 using several international databases and search engines. A Zotero library was set up consisting of 415 scientific references. Each reference was carefully read and tagged in several categories: cropping system, country, main tree species, intercrop type, intercrop product, level of product use, discipline of the study, research topic, and intercrop species. Of the 232 journal articles, 141 studies were carried out on rubber agroforestry. Since 2011, the number of studies per year has increased. Studies on rubber-based agroforestry systems are performed in most rubber-producing countries, in particular in Indonesia, Thailand, China, and Brazil. These studies focus more or less equally on perennials (forest species and fruit trees), annual intercrops, and mixed plantations. Of the 47 annual crops associated with rubber in the literature, 20 studies dealt with rice, maize, banana, and cassava. Agronomy is the main discipline in the literature followed by socio-economy and then ecology. Only four papers are devoted to plant physiology and breeding. The Discussion Section has attempted to analyze the evolution of rubber agroforestry research, progress in the selection of food crop varieties adapted to agroforestry systems, and to draw some recommendations for rubber-based agroforestry systems associated with food crops. [ABSTRACT FROM AUTHOR]

Most of the current crop row detection algorithms focus on extracting crop canopy rows as location information. However, for some high-pole crops, due to the transverse deviation of the position of the canopy and roots, the agricultural machinery can easily cause the wheel to crush the crop when it is automatically driven. In fact, it is more accurate to use the crop root row as the feature for its location calibration, so a method of crop root row detection is proposed in this paper. Firstly, the ROI (region of interest) of the crop canopy is extracted by a semantic segmentation algorithm, then crop canopy row detection lines are extracted by the horizontal strip division and the midpoint clustering method within the ROI. Next, the Crop Root Representation Learning Model learns the Representation of the crop canopy row and crop root row to obtain the Alignment Equation. Finally, the crop canopy row detection lines are modified according to the Alignment Equation parameters to obtain crop root row detection lines. The average processing time of a single frame image (960 × 540 pix) is 30.49 ms, and the accuracy is 97.1%. The research has important guiding significance for the intelligent navigation, tilling, and fertilization operation of agricultural machinery. [ABSTRACT FROM AUTHOR]

[Display omitted] • Sensitive : carbon dots were sensitive to pyrimethanil (PMT) yielding LOD of 14 nM. • Selective : carbon dots were selective to PMT against several interferences. • Accurate : concentrations of PMT in real samples were accurately determined. • Reusable : paper-based sensor produced excellent results even after 8-time uses. • Fast : the sensor gave the detection results within 30 s. Novel sensors based on fluorescent carbon dots were developed for a rapid, selective, and sensitive detection of pyrimethanil, a fungicide widely used in many agricultural crops. The carbon dots were synthesized from maleic anhydride and triethylenetetramine via a facile one-pot pyrolysis. A quenched fluorescence intensity of carbon dots in the presence of pyrimethanil occurred via inner filter effect and strong π-π interaction. The carbon dots were sensitive to pyrimethanil across a linear range from 0.5 to 75 µM with a low detection limit of 14 nM. The sensing results could be generated in 30 s. The fluorescent carbon dots showed excellent stability and selectivity to pyrimethanil in the presence of interferences, including fungicides and metal ions. They were also successfully employed for pyrimethanil detection in apples, cucumbers, and drinking water, yielding 98.6–107.1% recovery. The practical, portable, and reusable paper-based sensor was also developed. The cytotoxicity of carbon dots was tested to demonstrate that they are sufficiently safe to use. The novel pyrimethanil sensor based on fluorescent carbon dots developed in this work can be used as a high-performance sensor and a rapid tool for quality control in food, agricultural, and environmental monitoring. [ABSTRACT FROM AUTHOR]

This study addresses a very topical issue, climate changes, increasingly present recently, with negative effects on our entire existence, with repercussions on agriculture and the way food is produced in general. In the last decade, climate changes have been increasingly present and adapting to these changes is essential. In this paper, two crops, barley and alfalfa are presented, and how these climate changes produced in the Sânnicolau Mare area during 2020-2022 had negative effects on the soils and, above all, on the production of barley and the number of mows in alfalfa. In the elaboration of the paper, the bibliographic study was combined with a series of laboratory analyses regarding the properties of the two types of soil identified. The idea was developed on the basis that we monitored the annual thermal and pluviometric regimes impact on crops and on soil evolution, through their distribution and through the specific adaptability of the plants to risk conditions in a short period of time. These climatic changes are obvious not only from one year or from one season to another: they appear within the same locality bringing obvious changes in terms of the amount of precipitation in a certain area of that locality. [ABSTRACT FROM AUTHOR]

Avocado is one of the most commercialized and profitable fruits in the international market. Its cultivation and production are centered in countries characterized by tropical and subtropical climatic conditions, many of them with emerging economies. Moreover, the use of technology is key to agricultural production improvement strategies. Using avocado crop data to forecast the potential impacts of biotic and abiotic factors, combined with smart farming technologies, growers can apply measures during a single production phase to reduce the risks caused by pests and weather variations. Therefore, this paper aims to distinguish the most relevant variables related to agroclimatic and phytosanitary events in avocado crops, their incidence on production and risk management, as well as the emerging technologies used for the identification and analysis of pests and diseases in avocados. A scientific literature search was performed, and the first search found 608 studies, and once the screening process was applied, 37 papers were included in this review. In the results, three research questions were answered that described the pests and diseases with high impact on avocado production, along with the data sources and the principal enabling technologies used in the identification of agroclimatic and phytosanitary events in avocados. Some challenges and trends in the parameterization of the technology in field conditions for data collection are also highlighted. [ABSTRACT FROM AUTHOR]

Disease detection in agricultural crops, such as Solanum tuberosum L. (potato), is of utmost importance to ensure crop health and maximize yield. Traditional methods for disease detection in potatoes rely on manual inspection, which can be time-consuming and prone to human error . Image processing and machine learning techniques have shown promise in automating disease detection processes. This study proposes a novel approach for disease detection in Solanum tuberosum L. by leveraging image fusion techniques. The proposed method involves the fusion of multiple images of potato plants, acquired using different sensors or imaging modalities, to create a comprehensive and informative representation of the crop. Image fusion methods, such as discrete wavelet transform and continuous wavelet transform, are employed to combine the spectral and spatial information from the images effectively. The different image fusion rule is applied to the input images and resultant fused images, where relevant features are extracted to distinguish between healthy and diseased potato plants. The training dataset comprises diverse samples of both healthy and diseased potato plants, captured under various environmental conditions and disease stages. The performance of the proposed disease detection system is evaluated using standard metrics such as entropy. The results demonstrate the effectiveness of the image fusion approach in accurately identifying diseased potato plants, achieving high detection accuracy and generalization capabilities. The potential benefits of this paper include providing farmers and agricultural experts with an efficient and reliable tool for early disease detection in potato crops. Early detection can lead to timely intervention, minimizing crop losses and optimizing agricultural practices. The proposed methodology also lays the groundwork for future research in using advanced image processing techniques and machine learning algorithms for disease detection in other agricultural crops, contributing to the overall improvement of crop management and food security. [ABSTRACT FROM AUTHOR]

"European Green Architecture" refers to the implementation of sustainable agriculture that provides sufficient food and ecosystem services for both present and future generations. The conditions contained in eco-scheme PD-04 "Environmentally friendly practices for arable land" refer to the introduction of agricultural practices that have a positive impact on the environment and are aimed at farms with an area of more than 10.01 ha. The data was provided by the Agency for Payments and Interventions in Agriculture in Romania. The method of analysis was the comparative method, as well as the analysis of the structure of the cultivated and non-cultivated areas, the average for each county of Romania, the situation of the structure of the crops and the presence of nitrogen-fixing crops and protein crops, relevant indicators for the farms that applied for the PD-0 eco-scheme. The results of the analysis show that in 2023, the number of farms in Romania that have applied for the PD-04 eco-scheme is 46,608, with a total area of 5,789,746.35 ha, of which 2,934 were declared as uncultivated land, corresponding to 26,829.82 ha. According to the general agronomic rules, the crop rotation for field crops aims at an optimal crop structure as follows: 25% cereal straw, 25% maize, 25% legumes (soybeans, peas, beans, etc.) and sunflowers, 25% forage crops (alfalfa, clover, peas, etc.) necessary to increase soil fertilization. The paper presents the culture preferences of Romanian farmers in the year 2023, regarding the implementation of the eco-scheme PD-04 "Environmentally beneficial practices applicable to arable land". [ABSTRACT FROM AUTHOR]

The paper analyzed the dynamics of cultivated area, production and yield in vegetal sector of Romania's agriculture in the period 2007-2020 and also in the decade 2014-2020 versus 2007-2013 in order to quantify the effects of the implemented agricultural strategy aligned to the EU CAP. The data from National Institute of Statistics were processed using fixed basis indices, structural indices, growth rate, mean, regression models, R square and comparisons between the two decades. In 2020, Romania had 8,263,672 ha cultivated by +6.25% more than in 2007. Crop structure included: cereals 64.6%, oil plants 20.3%, vegetables 2.4%, leguminous plants 1.3% and 1% potatoes. The area cultivated with cereals raised by +4.07% reaching 5,338,067 ha in 2020, of which maize and wheat 87.9%. In the decade 2014-2020 versus 2007-2013, it was registered a larger total cultivated area and also with cereals (wheat and maize), leguminous crops, oil crops (sunflower), but a reduced area with vegetables and potatoes. Cereals production increased by +132.29% accounting for 18,153,714 tons in 2020, of which maize 55.61% and wheat 35.2%. In the period 2007-2020, important production gains were carried out in leguminous grains, oil seeds, fruits and vegetables, but potatoes production declined by -49.63%. In the period 2014-2020 versus 2007-2013, production gains were registered in cereals, wheat, maize, leguminous grains, in oil seeds, sunflower seeds and fruits, but losses by -4.14% in vegetables and -21.26% in potatoes. In 2020 versus 2007, yield level was accounted for 3,400 kg/ha cereals (+123.24%), 2,966 kg/ha wheat (+92.47%), 3,977 kg/ha maize (+160.61%), 1,858 kg/ha sunflower seeds (+184.09%), 15,843 kg/ha potatoes (+15.95%), 21,858 kg/ha tomatoes (+57.07%). In the period 2014-2020 versus 2007-2013, yield gain accounted for: +50.08% cereals grains, +44.7% wheat, +54.18% maize, +61.5% sunflower seeds, +13.13% potatoes and +12.83% tomatoes. In conclusion, the implementation of Romania's strategy of agriculture development in the vegetal sector aligned to CAP has resulted in higher productions and yields of the analyzed groups of crops, except potatoes production which declined by about 50% in 2020 compared to 2007, and also, in the 2nd decade 2024-2020 versus 2007-2013, it was registered a decrease by -4.14% in vegetables and by -21.26% in potatoes, which is justified by the decline in cultivated area. This performance is due to the efforts made by farmers to obtain better results in their business and to the financial support given by the EU and Romanian Government. [ABSTRACT FROM AUTHOR]

The research on Paulownia spp. has increased in the last twenty years thanks to the growing interest in the application modalities of this plant in various sectors such as wood, phytoremediation, environmental protection, paper, biofuel, chemistry and medicine. For the first time, this study analyzed the papers present in the Web of Science Core Collection on "Paulownia" to obtain a set of characteristics in the work carried out from 1971 to 2021. This analysis selected and took into account 820 articles and provided evidence of the scientific production of authors, institutions, and countries. This work showed that the most studied species was Paulownia tomentosa, followed by P. fortunei and P. elongate. The JCR category and research area with the most publications was plant science, with 20.4% of the total. The papers were published in 460 journals and in a book series. The journals with the most publications were Bioresources, Advanced Material Research, Agroforestry Systems, Journal of Wood Science and Industrial Crops and Products. The institutions with the most prolific affiliation with the field of Paulownia spp. research were Henan University, the US Department of Agriculture, Belgrade University, the Chinese Academy, and Georgia University. Finally, the 3842 keywords were divided into nine different clusters and the trends of interest in the last fifteen years were highlighted. [ABSTRACT FROM AUTHOR]

This editorial summarizes the main scientific contributions from 11 papers comprising the Special Issue (SI) "Molecular Basis of Crops and Fruit Plants in Response to Stress". Here, we collected papers from different research groups encompassing molecular studies from monocots (ginger, rice, maize) and eudicots (common hazel, cowpea, pepper, soybean, tomato) species submitted to abiotic stresses as heat, cold, salt, drought, and heavy metals or biotic stresses induced by different viruses, such as BPEV, PepGMV, PMMoV, and TEV. These studies explored different aspects of molecular mechanisms involved in plant stress tolerance, establishing comparative analyses among genotypes/cultivars to identify potential molecular markers of stresses that are now available for future application in biotechnological studies. This SI presents a collection of advanced concepts and emerging strategies for readers and researchers aiming to accelerate plant breeding. [ABSTRACT FROM AUTHOR]

One of the ideas behind vegetation monitoring is the ability to identify different vegetation units, such as species, communities, habitats, or vegetation types. Remote sensing data allow for obtaining such information remotely, which is especially valuable in areas that are difficult to explore (such as mountains or wetlands). At the same time, such techniques allow for limiting field research, which is particularly important in this context. Remote sensing has been utilized for vegetation inventories for many decades, using airborne and spaceborne platforms. Developing newer tools, algorithms and sensors is conducive to more new applications in the vegetation identification field. The Special Issue "Remote Sensing Applications in Vegetation Classification" is an overview of the applications of remote sensing data with different resolutions for the identification of vegetation at different levels of detail. In 14 research papers, the most frequent different types of crops were analysed. In three cases, the authors recognised different types of grasslands, whereas trees were the object of the studies in two papers. The most commonly used sensors were Copernicus Sentinel-1 and Sentinel-2; however, to a lesser extent, MODIS, airborne hyperspectral and multispectral data, as well as LiDAR products, were also utilised. There were articles that tested and compared different combinations of datasets, different terms of data acquisition, or different classifiers in order to achieve the highest classification accuracy. These accuracies were assessed quite satisfactorily in each publication; the overall accuracy (OA) for the best result varied from 72% to 98%. In all of the research papers, at least one of the two commonly used machine learning algorithms, random forest (RF) and support vector machines (SVM), was applied. Additionally, one paper presented software ARTMO's machine-learning classification algorithms toolbox, which allows for the testing of 13 different classifiers. The studies published in this Special Issue can be used by the vegetation research teams and practitioners to conduct deeper analysis via the utilization of the proposed solutions. [ABSTRACT FROM AUTHOR]

The present paper addresses a topical issue for the agricultural production sector and it is based on an economic-financial indicators analysis for a series of 17 main agricultural crops. The practical research was carried out with information and data for 10 agricultural holdings in Iaşi County. They operate in the vegetable production sector and have a solid commercial representation on the national market. The main indicators used in the study are: crops and shared areas for each of the 10 analyzed units, considering the relevance of crops for the national economy and economic-financial indicators relevant for the design of the production activity: turnover, profit, employes number. The reference year for which the research was carried out is 2021. The work methodology mainly uses quantitative methods and descriptive analysis, with the processing and interpretation of data that have been organized in a design configured according to the specifics of the paper. In addition, qualitative information collected through interviews is used. The results of the paper indicate the relevance of basic agricultural crops for the performance of large enterprises. [ABSTRACT FROM AUTHOR]

Weather extremes have been responsible for widespread economic damage at global scale in the last decades. Agriculture alone absorbed 26% of the overall impact caused by natural hazards in low- and middle-income countries and even in high-income countries yield losses due to extreme weather are relevant. Vulnerability curves are traditionally used to quickly estimate the damage due to extreme events. This study maps the articles published from January 2000 to May 2022 implementing crop vulnerability curves to weather-related extreme events and climate change. Fifty-two articles have been identified through the use of Scopus, Web of Science, Google Scholar and the references of the selected papers. The selected papers have been analysed to determine for which extreme events vulnerability curves have been proposed, which crops have been studied, which explanatory variables have been used to create the curves, which functions are used to develop vulnerability curves and the number of parameters on which the proposed functions rely. Comparisons among the vulnerability curves for the various extremes are proposed, as well as indications of the main drawback of the developed vulnerability curves. Finally, areas where further research is needed are proposed together with recommendations on which elements should be included in vulnerability curve development. [ABSTRACT FROM AUTHOR]

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This paper explores illegal coca crop expansion in indigenous Amazonian communities in Peru. The ethnographic study sheds light on the historical development of these areas as frontier spaces, where the growth of illicit crops intertwines with socio-ecological transformations and gives rise to conflicts over new forms of land control, opportunities for capital accumulation, and political power dynamics. The paper argues that this expansion is shaped by dual processes: from 'below,' involving small-scale migrant farmers from the Andes, and from 'above,' primarily driven by state-led agrarian interventions. Consequently, communities experience significant tensions, as they adapt to the forces of market expansion to secure their livelihoods, while simultaneously facing risks of violence and insecurity. [ABSTRACT FROM AUTHOR]

Vertical Farming System (VFS) is an innovative method of agricultural production that combines crop cultivation with construction, often in indoor environments such as buildings, warehouses, or other urban structures, so that the produce from vertical farms can be supplied to city dwellers with reduced transport costs and carbon emissions. Vertical farms feature vertically stacked, three-dimensional planting racks, where each level can be planted with the appropriate crop, and where all environmental and crop growth factors are monitored and controlled. Vertical farms can help to solve the current food and vegetable supply and supply chain problems caused by the increasing global population, global influenza and growing regional conflicts. VFS are highly automated and therefore robots of all types are used in them, from planting, fertilizer application, growth monitoring, harvesting, and transporting. Many robotic systems have been designed and developed by researchers around the world for various scenarios in agricultural production, and many robots are suitable for use in VFS. This paper provides an overview of agricultural robots that have been used by researchers around the world over the past 5 years to detect or monitor the agricultural environment and crop growth data, as well as robots that perform agricultural operations, and some agricultural robots that have not been developed for VFS but have the potential to be used in VFS through simple modifications. The results of the study may allow VFS practitioners, producers, and other stakeholders to learn more about the current level of robotics development in the industry and to adopt more automated production methods to improve the efficiency and economic benefits of VFS. [ABSTRACT FROM AUTHOR]

The purpose of the paper was to assess the production capacity of the lands in Periam, Timiș County, in wheat, barley, corn, and sunflower. We chose 17 more important and better determining indicators and, based on them, we established, for each crop, the credit ratings, after which we classified the soils in the studied area into fertility classes. In accordance with the methodology for developing pedological studies, the degree of suitability was determined for each of the four crops: 91.73% of the area of this locality is represented by agricultural land, of which arable land represents 79.22%. and 0.41% is forested land. Following the evaluation of arable land (4,663 ha), it was found that 45.38% fall into the first class, 33.63% in Class II, 20.10% in Class III, and 0.89% in Class IV, with an average grade of 75. This makes these lands highly productive. As a general conclusion, each farmer should periodically evaluate the fertility of the farm soils, make a balance of the nutrient requirements for each crop by considering the plants' requirements according to the vegetation phase, the technology applied, the local soil land climate conditions based on realistic forecasts. In this way, a series of costs with fertilizers and soil work are reduced, the excess is eliminated, and the deficiency of nutrients is corrected, the recommended doses are fractionated and applied differently depending on the needs of the plant, the humidity, the reaction and the texture of the soil. Following the results obtained, it appears that these lands have a high production capacity. [ABSTRACT FROM AUTHOR]

The paper presents the analysis of the profitability threshold of bell pepper cultivation in the solar system in conventional farming and organic farming with proposed for the crop year 2022/2023. Thus, by using a system of indicators (specific indicators, result indicators and profitability indicators, production costs, capitalization prices and the degree of profitability per product unit can be estimated. Economic profitability is evaluated in terms of the use of production framework technologies in the production process and the rational use of raw materials, human and financial resources with the aim of obtaining low-cost and high-quality products. Agribusiness actions will always be based on crop profitability. The findings of the undertaken study can favourably influence farmers, giving them the opportunity to adjust their existing farm resources and capacities to achieve higher yields. [ABSTRACT FROM AUTHOR]

The problem of image-based detection of the variety of beans, using artificial intelligence, is currently dealt with by scientists of various profiles. The idea of this paper is to show the possibility of applying different types of distances, primarily those that are fuzzy metrics, in clustering models in order to improve existing models and obtain more accurate results. The paper presents the method of variable neighborhood search, which uses both standard and fuzzy t-metrics and dual fuzzy s-metrics characterized by appropriate parameters. By varying those parameters of the fuzzy metric as well as the parameters of the metaheuristic used, we have shown how it is possible to improve the clustering results. The obtained results were compared with existing ones from the literature. The criterion function used in clustering is a fuzzy metric, which is proven in the paper. [ABSTRACT FROM AUTHOR]

Food security has become a concerning issue because of global climate change and increasing populations. Agricultural production is considered one of the key factors that affects food security. The changing climate has negatively affected agricultural production, which accelerates food shortages. The supply of agricultural commodities can be heavily influenced by climate change, which leads to climate-induced agricultural productivity shocks impacting crop prices. This paper systematically reviews publications over the past ten years on the factors affecting the prices of a wide range of crops across the globe. This review presents a critical view of these factors in the context of climate change. This paper applies a systematic approach by determining the appropriate works to review with defined inclusion criteria. From this, groups of key factors affecting crop prices are found. This study finds evidence that crop prices have been both positively and negatively affected by a range of factors such as elements of climate change, biofuel, and economic factors. However, the general trend is towards increasing crop prices due to deceasing yields over time. This is the first systematic literature review which provides a comprehensive view of the factors affecting the prices of crops across the world under climate change. [ABSTRACT FROM AUTHOR]

Currently, there is a wealth of data and expert knowledge available on monitoring agro-meteorological disasters. However, there is still a lack of technical means to organically integrate and analyze heterogeneous data sources in a collaborative manner. This paper proposes a method for monitoring agro-meteorological disasters based on a spatio-temporal knowledge graph. It employs a semantic ontology framework to achieve the organic fusion of multi-source heterogeneous data, including remote sensing data, meteorological data, farmland data, crop information, etc. And it formalizes expert knowledge and computational models into knowledge inference rules, thereby enabling monitoring, early warning, and disaster analysis of agricultural crops within the observed area. The experimental area for this research is the wheat planting region in three counties in Henan Province. The method is tested using simulation monitoring, early warning, and impact calculation of the past two occurrences of dry hot wind disasters. The experimental results demonstrate that the proposed method can provide more specific and accurate warning information and post-disaster analysis results compared to raw records. The statistical results of NDVI decline also validate the correlation between the severity of wheat damage caused by dry hot winds and the intensity and duration of their occurrences. Regarding remote sensing data, this paper proposes a method that directly incorporates remote sensing data into spatio-temporal knowledge inference calculations. By integrating remote sensing data into the regular monitoring process, the advantages of remote sensing data granted by continuous observation are utilized. This approach represents a beneficial attempt to organically integrate remote sensing and meteorological data for monitoring, early warning, and evaluation analysis of agro-meteorological disasters. [ABSTRACT FROM AUTHOR]

Myxomycetes (plasmodial slime molds) are eukaryotic protist predators that are associated with wood, leaf litter, and soil in forests, where they feed on bacteria, protozoans, and (to a more limited extent) fungi. The health of crop plants is essential because they represent a primary food source for humans. However, when myxomycetes produce numerous fruiting bodies on the stems and leaves of crop plants, which is herein referred to as a myxomycete colonization, this has the potential of interfering with plant photosynthesis, transpiration and respiration by blocking out light and covering stomata. Myxomycetes are not pathogens, but their occurrence on plants can be mistakenly interpreted as some type of infection. However, this phenomenon has been largely ignored. This paper provides a comprehensive overview of the taxonomic and economic diversity of the organisms involved in myxomycete colonization. In addition, the various types of myxomycete colonization reported in the literature are described and discussed, a number of images provided, and cultural and chemical prevention and control measures are summarized. The latter should be of significant relevance for local production of crops and plant protective stations. While myxomycetes are not pathogens of crop plants, some species can seriously impact commercially grown mushrooms. Reports of myxomycetes affecting mushrooms are also described in this paper. [ABSTRACT FROM AUTHOR]

The continued expansion of the world population, increasingly inconsistent climate and shrinking agricultural resources present major challenges to crop breeding. Fortunately, the increasing ability to discover and manipulate genes creates new opportunities to develop more productive and resilient cultivars. Many genes have been described in papers as being beneficial for yield increase. However, few of them have been translated into increased yield on farms. In contrast, commercial breeders are facing gene decidophobia, i.e., puzzled about which gene to choose for breeding among the many identified, a huge chasm between gene discovery and cultivar innovation. The purpose of this paper is to draw attention to the shortfalls in current gene discovery research and to emphasise the need to align with cultivar innovation. The methodology dictates that genetic studies not only focus on gene discovery but also pay good attention to the genetic backgrounds, experimental validation in relevant environments, appropriate crop management, and data reusability. The close of the gaps should accelerate the application of molecular study in breeding and contribute to future global food security. [ABSTRACT FROM AUTHOR]

The accurate identification of crop aphids is an important aspect of improving agricultural productivity. Aphids are characterised by small targets and a body colour similar to their surroundings. Even the most advanced detectors can experience problems such as low detection accuracy and a high number of missed detections. In this paper, a multi-stream target detection model is proposed for fast and accurate detection of crop aphids in complex backgrounds. First, inspired by the human visual system, we propose a bionic attention (BA) approach. Unlike previous strategies, we do not improve the model but input additional category labels as bionic information streams into the network at the network input stage to support mainstream recognition, which improves the detection effect. In addition, through web crawling and manual screening, we construct an aphid dataset containing 2059 images, named IP_Aphids, based on IP102. In this paper, we combine the proposed BA with a number of classical target detection models, including YOLOv5s, YOLOv7-tiny, YOLOv8n, SSD, and faster R-CNN, and we also compare the BA with the classical attention mechanisms CBAM and SE. This approach allows the effectiveness of the method to be evaluated from multiple perspectives. The results show that combining the bionic information flow of BA with the original mainstream information flow as inputs to the five target detection models improves the mAP by 2.2%, 7%, 2.7%, 1.78%, and 1.12%, respectively, compared to the models using only the original inputs. In addition, the mAP of the YOLOv8n_BA model is higher than that of the YOLOv8n, YOLOv8n_CBAM, and YOLOv8n_SE models by 4.6%, 3.3%, and 2.7%, respectively. This indicates that the BA proposed in this study is significantly better than the classical attention to improve crop aphid detection, which provides a reference for crop aphid-related research. [ABSTRACT FROM AUTHOR]

During the past 30 years, there has been a growing belief in and promotion of agroecosystem diversity for pest management and future food production as an agroecological or nature-based approach. Monoculture agriculture, which produces most of our food, is considered to be highly vulnerable to pests in contrast to plant species-diverse agroecosystems which may possess a greater abundance of natural enemies, keeping pest populations under control. In this paper, we question the role of crop diversity for pest management and explore the relationship between crop and associated diversity and pests through the following processes: environmental stresses that favor monodominance; evolutionary adaptations that resist insect herbivores (genetic resistance response); mechanisms of spatial escape from insect herbivores (escape response); and the role of crop-associated biodiversity. We present strong evidence that not only questions the high vulnerability of monocultures to pest damage but also supports why monocultures continue to produce most of the world's food. Reference is made to the importance of targeted plant breeding and the role of trans-continental crop introduction supported by efficient quarantine for pest management. We conclude that—with the exception of irrigated rice—much more research is needed to better understand the role of crop diversity in agroecosystems for pest management and food production. [ABSTRACT FROM AUTHOR]

AGRICULTURE productivity is severely hampered by salinity stress in many parts of the world, including the Ararat Plain in Armenia. The need for food security is growing worldwide, and new strategies are essential to meet this challenge. In the context of the Ararat Plain, this paper examines the potential utility and promising role of biogenic nanoparticles (NPs) and biochar in reducing salinity stress, boosting crop resilience, and guaranteeing sustainable food production. This paper involves the mechanisms underlying salinity stress, as well as the properties and synthesis of biogenic nanoparticles and biochar, and their applications in increasing plant salinity tolerance. The potential application of biogenic nanoparticles and biochar to mitigate the adverse effects of salinity on crops, soil, and water resources has been recently illustrated by several case studies and research findings. Additionally, we go over the safety issues, environmental implications, and potential applications of biogenic nanoparticles and biochar in sustainable agriculture on the Ararat Plain and elsewhere. This paper advances our knowledge of innovative strategies for addressing salinity stress and improving food security in areas with comparable problems. [ABSTRACT FROM AUTHOR]

The paper deals with index-based crop insurance as a tool for managing flood and drought risks in agriculture. We introduce a novel model for determining premium rates in index-based crop insurance which combines the loss cost method and the average yield method. The proposed model was applied to data related to the production of selected crops in Serbia to calculate unique premium rates for index-based insurance for cereals, industrial crops and fodder plants. The paper also outlines the prerequisites for index-based crop insurance to become technically, operationally, and financially feasible in Serbia. We propose the introduction of mandatory index-based crop insurance, along with necessary legislative amendments and subsidization of insurance premiums. [ABSTRACT FROM AUTHOR]

Agricultural cropping structure is related to the quality of the agricultural supply system and is a key element of the structural reform of the agricultural supply side. Based on China's provincial panel data from 2000 to 2021, this paper empirically examines the impact and mechanism of rural population aging on the planting structure of food and cash crops by using a two-way fixed-effects model, which fills the gap in the research on the impact mechanism of the rural population aging on agricultural planting structure. The conclusions of the study show that: as the aging of the rural population deepens, the proportion of food crops planted will further increase, while the proportion of cash crops planted will decrease; agricultural mechanization will promote the further increase of the proportion of food crops planted while the proportion of cash crops planted will decrease in the deepening of the aging of rural population; the aging of the rural population has a more significant impact on the structure of agricultural planting in the eastern region and does not have a significant impact on the central and western provinces. The aging of the rural population has a significant impact on the agricultural planting structure in the eastern region, but not in the central and western provinces. This paper argues that we should fully respect the willingness of agricultural management subjects to choose planting varieties, increase the research, development, and promotion of agricultural machinery, continuously improve the level of farmers' human capital, and further enhance the degree of organization, scale, and specialization of agricultural production. [ABSTRACT FROM AUTHOR]

The article discusses the importance of crop tolerance to both abiotic and biotic stresses in agricultural research. Abiotic stresses include environmental factors like drought and extreme temperatures, while biotic stresses involve pests, diseases, and pathogens. Understanding and enhancing crop tolerance to these stresses is crucial for global food security and sustainable crop production. The article highlights various research papers from different countries that focus on topics such as abiotic stress (salinity, drought) and biotic stress (pests, diseases). The research papers explore different strategies, including breeding and genetic engineering, to enhance crop tolerance. [Extracted from the article]

Remote sensing technology has become a popular tool for crop classification, but it faces challenges in accurately identifying crops in areas with fragmented land plots and complex planting structures. To address this issue, we propose an improved method for crop identification in high-resolution remote sensing images, achieved by modifying the DeepLab V3+ semantic segmentation network. In this paper, the typical crop area in the Jianghuai watershed is taken as the experimental area, and Gaofen-2 satellite images with high spatial resolutions are used as the data source. Based on the original DeepLab V3+ model, CI and OSAVI vegetation indices are added to the input layers, and MobileNet V2 is used as the backbone network. Meanwhile, the upper sampling layer of the network is added, and the attention mechanism is added to the ASPP and the upper sampling layers. The accuracy verification of the identification results shows that the MIoU and PA of this model in the test set reach 85.63% and 95.30%, the IoU and F1_Score of wheat are 93.76% and 96.78%, and the IoU and F1_Score of rape are 74.24% and 85.51%, respectively. The identification accuracy of this model is significantly better than that of the original DeepLab V3+ model and other related models. The proposed method in this paper can accurately extract the distribution information of wheat and rape from high-resolution remote sensing images. This provides a new technical approach for the application of high-resolution remote sensing images in identifying wheat and rape. [ABSTRACT FROM AUTHOR]

Crop divider toes are an essential device of sugarcane harvester. Moving forward against the ground is a critical way to improve the harvesting rate of lodged sugarcane. Height detection is the basis for precise control of crop divider toes moving forward against the ground. Due to the current problem of operating difficulties in manually adjusting the height of crop divider, a height detection system based on a millimeter wave radar sensor was designed to detect the height of crop divider toes from the ground. This paper proposed a height detection method of crop divider toes for sugarcane harvester based on Kalman adaptive adjustment. The data measured by the sensor was pretreated to determine whether the height had changed. Reset the Kalman filter and adjust the parameters when changes occur to improve the filter response speed and ranging accuracy. To adapt to the scenario of quickly adjusting the height of crop divider during the traveling process of sugarcane harvester. A one-way ANOVA test and a two-way ANOVA test were conducted on a simulated test platform. The results of the one-way ANOVA test showed that both forward speed and vegetation cover thickness had a significant effect on height detection accuracy. The results of the two-way ANOVA test showed that the interaction of forward speed and vegetation cover thickness did not have a significant effect on ranging accuracy. It was verified through experiments that both the ranging accuracy and the response speed of height change were significantly improved after the processing of the method in this paper. The mean square error after processing was lower than 2.5 cm. The feasibility of the height detection system was verified by field trials. The results of this study will provide a reference for the design of automatic elevation of crop divider. [ABSTRACT FROM AUTHOR]

Reducing the amount of industrial crop waste and converting it into high value-added products in a sustainable manner will not only promote the use of biological resources but will also help in reducing the environmental impact. Bast fibers of Kozo (Broussonetia kazinoki) are a raw material of traditional Japanese paper, known as "Washi." However, the wood part of this plant is not used for Washi and is discarded. The aim of this study was to find a new future use for the wood parts of the plant. The first step was to confirm whether the wooden part of Kozo could be converted into cellulose nanofibers (CNFs). CNFs made from bast fiber alone and from bast fiber that included the outer bark were prepared as samples for comparison. Subsequently, the morphological and chemical structures of the raw materials, wood powder, pulp, and CNFs from each sample were analyzed. Consequently, we found that CNFs made from wooden parts have a chemical structure similar to that of the bast fiber, which is the raw material of Washi; however, it is the thinnest CNF of all those investigated. In addition, the CNF solution prepared from wooden parts adjusted to a 0.5-wt% concentration showed the highest viscosity characteristics and also the highest adhesive strength when applied to Japanese paper. These results indicate that waste wood parts have potential applications as chemical-free thickeners, adhesives, and restorative materials. [ABSTRACT FROM AUTHOR]

The paper purpose was to assess the impact of climate change on wheat, maize and sunflower yield in the period 2017-2021, using statistical data from National Institute of Statistics (NIS) and National Administration of Meteorology (NAM) and other sources. Comparison method was used to evaluate the deviations between the registered air temperatures and precipitations and the 1981-2010 climatological norm. Graphic method reflected the dynamics of monthly air temperatures and rainfalls in each year. Descriptive statistics for mean, standard deviation and coefficient of variation reflected an more comprehensive image upon air temperature, precipitations and yield. Correlations and regression equations were used to establish the intensity of the kinks between climate factors and yield. The highest average air temperature In Romania was 12.130C in 2019. In the period 2017-2020, the average annual temperatures exceeded the climatological norm 1981-2010 accounting for 9.10C. The lowest rainfalls, 614.2 mm, were recorded in 2019, being below of 633.1 mm the norm for the period 1981-2010. The high temperatures associated with drought and low precipitations have deeply affected yield of many agricultural crops, including maize, wheat and sunflower. The highest wheat yield 4,888 kg/ha was achieved in 2017, and the lowest one, 2,966 kg/ha in 2020. Maize registered the highest performance of 7,644 kg/ha in 2018, and the lowest one, 3,977 kg/ha in 2020. Sunflower carried out the high performance 3,041 kg/ha in 2018, and the lowest one, 1,858 kg/ha in 2020. The worst agricultural years for these three crops was 2020, but 2017 favored wheat, while 2018 favored maize and sunflower. The correlation coefficient had in general small values between average temperature and yield, but a higher link with precipitations level. The multiple correlation between yield, air temperatures and precipitations was: r= 0.817 for wheat, r = 0.116 for maize and r = 0.504 for sunflower. In the South Eastern Dobrogea, the driest area in Romania, the rainfalls declined by 65% in the period 2018-2020 having a deep negative impact on sunflower seeds yield, which decreased from 4,282 kg/ha in 2018, to 1,503 kg/ha in 2020. The correlation between precipitations and sunflower yield positive and very strong (r = 0.737). As a conclusion, the climate conditions should be analyzed in each farm and farmers have to take measure to adapt the technologies for sustaining production. [ABSTRACT FROM AUTHOR]

Pedoclimatic elements and smart technology act simultaneously on the biological processes of agricultural crops and on the level of harvest, which requires the study of their correlation to determine the continuous influence of these factors in obtaining the planned levels of production. In Romania, annually, over 2.1 million hectares of wheat are cultivated, with annual productions of over 10 million tons, placing wheat crops in the first place as economic importance. Knowing the management of natural resources in quantitative terms is especially valuable when we talk about their sustainable use. Our evaluation includes in this paper the relationship between the pedoclimatic resources of South-Eastern Romania and the zoning of wheat varieties in order to obtain superior quantitative and qualitative productions. The last 11 agricultural years are analyzed, both in terms of the influence of global warming and in terms of wheat zoning to obtain sustainable production to highlight the genetic potential of the varieties under the conditions of the Brailei Plain. A stability of the varieties Glosa, Miranda, Litera (varieties over 15 years old) was observed, but not least of the new ones created by the National Research and Development Institute from Fundulea such as: Pitar, Ursita, Adelina, Izvor. [ABSTRACT FROM AUTHOR]

Obtaining genetically modified (GM) organisms, with superior biological and productive performances, represents the priority objective of modern applied genetics research, oriented towards the development of effective procedures for increasing genetic variability, according to the requirements of breeding programs of economically interesting species. GM crops can contribute essentially to the millennium development goal, that of reducing poverty and increasing food security, by optimizing agricultural productivity. GM plants have improved traits that include herbicide tolerance, disease and pest resistance, drought tolerance, health or nutritional benefits, a longer shelf life, or a more efficient industrial use. Also, the GM crops contribute to sustainable environmental protection by reduction of the pesticides application amount and reduction of CO2 emissions. In this context, the aim of the present paper is the worldwide brief radiography of transgenesis, in terms of surfaces, the main producing countries, but also of the main GM crops and their market. The research method consisted in selecting of several scientific results from the WOS, Clarivate Analytics, Scopus and Springer databases. Also, were accessed several statistical data of the ISAAA, USDA, Research and Market, MADR, etc. The main producing countries of GM crops are USA, Brazil, Argentina, Canada, India, Paraguay and China. The largest GM areas are occupied by soybean, maize, cotton, canola and alfalfa. The global agricultural biotechnology market for transgenic crops is expected to reach 12.07 billion USD in 2026, growing by 18.2% compared with 2021. Genetic transformation and GM agricultural crops represent an effective strategy and real chance to counteract climate change and food insecurity, and recently developed genetic engineering techniques will play an important role in the future. [ABSTRACT FROM AUTHOR]

Effects of Bacillus thuringiensis (Bt) cotton are at the forefront of an intense debate on the benefits of genetically modified (GM) crops among smallholder farmers in developing countries. Existing studies fail to control for confounders, selection bias, or cultivation bias from preferential treatment in the initial adoption phase. Addressing these concerns in this paper, I examine the impact of Bt cotton employing an unbalanced panel fixed-effects model of a crop yield and profit function on newly collected plot-level data in the most recent decade. Results show that Bt cotton yields have stagnated, have a null effect on profits, and have become more sensitive to pest pressure in the most recent decade. Though many studies have demonstrated higher crop yield and profit gains in the first decade of Bt cotton adoption that raised the average returns to the technology, the second decade shows convergence in benefits, which raises obvious questions about the prospect of GM technology. Since Bt cotton is the only GM crop technology widely adopted by smallholder farmers, the findings of this paper contribute to the broader public debate on the future of agricultural biotechnology. [ABSTRACT FROM AUTHOR]

The profitability of maize cultivation depends on the production yield of this crop. This paper presents the analysis of the yield and profitability of maize cultivation in three crop farms of different sizes in Romania. From the analysis carried out, a number of results are obtained, which lead to the conclusion that in order to reach the profitability threshold, the production in the 60 ha farm would have to be at least 133.26 tons of maize in 2019, at least 85.83 tons of maize in 2020, and at least 165.56 tons of maize in 2021; in the 600 ha farm, the production in 2019 would have to be at least 2.350.23 tons of maize in 2019, 2,198.44 tons of maize in 2020, and 3,172.33 tons of maize in 2021; and in the 3,000 ha farm, the production should have been at least 4,157.75 tons of maize in 2019, 4,424.70 tons of maize in 2020, and 6,284.46 tons of maize in 2021. These productions were made only by the farm of 3,000 ha, which had a constant profitability of the maize crop, exceeding the profitability threshold every year, as follows: in 2019 with 2,216.85 tons of maize, in 2020 with 2,738.60 tons of maize and in 2021 with 3,887.34 tons. On farms of 60 ha and 600 ha, the profitability threshold could not be reached, in most cases, the major causes being of agropedoclimatic and economic nature. The conclusion of the analysis carried out was that, in order to have a constant profitability of the maize crop, it is necessary to counteract the drought by introducing irrigation to this crop, reducing production expenses and increasing the value of subsidies, so that the gross product highlights the profitability of the maize crop. [ABSTRACT FROM AUTHOR]